Unverified50% confidenceFactExact time
Each of Jev's answers comes with a confidence probability, which is essentially the softmax output value of the model for each option.
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50%
Confidence
Long-term
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10/6/2026
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UnverifiedThe core mechanism of large model inference is the continuous prediction of the probability of the next word, converting raw scores into a probability distribution summing to 1 via the Softmax function.77% similarUnverified模型对所有Token计算原始得分Logit后，通过Softmax函数将得分归一化为概率分布，所有Token的概率之和等于174% similarUnverifiedFor multiple-choice questions, the logits over each option token can be normalized via softmax to obtain a probability distribution, where a highly concentrated distribution indicates the model is more confident in the answer72% similarUnverifiedJev's core capability is to output classifications, probabilities, and rapid decisions, returning results at the millisecond level.71% similarUnverifiedExisting LLMs optimize for "human preference," whereas new models like Jev optimize for "calibrated decisions"—that is, cognitively honest probabilistic answers.70% similar
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